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864 results for “numerative”
FIGURE 1 in Numerical taxonomy and genus-species identification of Czekanowskiales in China based on machine learning
FIGURE 1. Distribution of Mesozoic Czekanowskiales fossils in China (Chinese basemap from the Standard Map Ser- vice, plan approval number: GS (2020) 4619).
FIGURE 4 in Numerical taxonomy and genus-species identification of Czekanowskiales in China based on machine learning
FIGURE 4. Trait importance scores for genus and species identification and heatmap of correlations between traits. (A) Importance scores for traits identified genus and species, with the solid symbols indicating key traits and the hollow symbols indicating nonkey traits; (B) proportion of importance scores for macro traits and cuticular traits identified at the genus and species; (C) heatmap of correlations between traits; serial numbers correspond to traits in Table 2.
FIGURE 5 in Numerical taxonomy and genus-species identification of Czekanowskiales in China based on machine learning
FIGURE 5. Accuracy distributions of five supervised learning algorithms and confusion matrix of the best algorithm. (A) Accuracy distribution of the five algorithms in genus-level identification (using mixed-key traits); (B) CART algorithm confusion matrix (using mixed-key traits); (C) accuracy distribution of five algorithms in genus-level identification (using only macro-key traits); (D) CART algorithm confusion matrix (using only macro-key traits); (E) accuracy distribution of five algorithms for species-level identification (using mixed-key traits); (F) LR algorithm confusion matrix (using mixed-key traits); (G) accuracy distribution of five algorithms for species-level identification (using only macro-key traits); (H) CART algorithm confusion matrix (using only macro-key traits). Genus abbreviations: Cz., Czekanowskia; Ph., Phoenicopsis; Sp., Sphenarion; So., Solenites.
FIGURE 2 in Numerical taxonomy and genus-species identification of Czekanowskiales in China based on machine learning
FIGURE 2. Fossils and schematic diagram of the leaf morphology and epidermal structure of Czekanowskia les. (A) Phoenicopsis angustifolia; (B) Czekanowskia rigida; (C) Phoenicopsis speciosa; (D) schematic diagram of the leaf morphology of Czekanowskiales, dotted line ab indicating the leaf cluster length (or lobe length), dotted line cd indicating the leaf cluster width, line ef indicating the lobe width, and angle cbd indicating the angle between bc and bd (the most lateral lobes); (E) schematic diagram of the epidermal structure of Czekanowskiales. Scale bars (A-C) equal 1 cm.
Uranium-series disequilibria in MORB, revisited: A systematic numerical approach to partial melting of a heterogeneous mantle
<p><span>This directory includes all of the gridded run results for each melting scenario for this paper, organized into approx. 50 supplementary figures with a series of activity ratio diagrams.</span></p>
Dataset for "The Ithildin library for efficient numerical solution of anisotropic reaction-diffusion problems in excitable media"
<p>This archive contains the full source code of Ithildin as well as the data generated by the simulations used in the paper introducing the Ithildin software.</p>
Fig. 2 in Marine and freshwater taxa: some numerical trends
Fig. 2 - Confidence intervals of the previous data illustrated by the Clopper-Pearson method. On the y axis: Frequency.
Fig. 1 in Marine and freshwater taxa: some numerical trends
Fig. 1 - Relative abundance of marine, amphibiotic and freshwater taxa. (Abscissa – taxa; ordinate – relative number of taxa in percent).
Figure 4. (a) Therapy player software screen, where a) is the stimuli time, b) is the total therapy time, c) is the file path, d) displays the numeric values of each sequence of the therapy, e) shows the current value, and f) shows the current lag angle for zenith and azimuth values; (b) USB mechanism for conversion, where a) USB-UART converter, and b) USB-Zigbee converter.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>Where tt time expended by the servomotors to point the laser to a given position and execute<br> a laser beam sequence; tspin is the time that a servomotor needs to spin one degree; ttol is a given the<br> tolerance time; θservo is the addition of degrees that both servos in a laser driver need to spin point<br> the laser in a given position; tstimuli is the time expended in execute a laser beam, between 250 and<br> 605 ms (Weiskrantz et al., 1991); T is the total time of all repetitions in a therapy, suggested<br> between 20 and 60 minutes and N is the number of repetitions in a therapy.</p>
Numerical dataset for the article "Single-photon absorption by single photosynthetic light-harvesting complexes"
<p>The numerical data for all figures presented in the article "Single-photon absorption by single photosynthetic light-harvesting complexes" published in the Journal of Physics B: Atomic, Molecular and Optical Physics (2018). Data include calculation runtimes and hardware specification, and are provided in csv. format.</p>
Complete numerical solutions for "Inference of ecological and social drivers of human brain-size evolution" by Mauricio González-Forero and Andy Gardner
<p>This zip file contains the complete numerical solutions across the parameter sweep over the P parameters for the six cases considered.</p> <p>/1RatioForm/ -> solutions for power competence.<br> /2DiffForm/ -> solutions for exponential competence.</p> <p>/1RatioForm/1BenchmarkFromSimpleInitialGuess -> solution for the step 1 of initialization (section 5 of the SI).<br> /1RatioForm/2Benchmark/ -> solution for the step 2 of the initialization (section 5 of the SI).<br> /1RatioForm/3AdditiveCoop/ -> solutions across the P parameter combinations for additive cooperation.<br> /1RatioForm/4MultCoop/ -> solutions across the P parameter combinations for multiplicative cooperation.<br> /1RatioForm/5SubMultCoop/ -> solutions across the P parameter combinations for submultiplicative cooperation.</p> <p>The contents of /2DiffForm/ are analogous.</p> <p>The P vector is written in these files in the order (etas,etac,etaC,etag), where<br> etas -> P1<br> etac -> P3<br> etaC -> P2<br> etag -> P4</p> <p>The files 1runNotesACMC.pdf and 2runNotesSC.pdf contain the tree structure of the parameter sweep, specifying which parameter combination was used as the resident and which combinations converged to an uninvadable strategy (those with a checkmark).</p> <p>The file 3runNotesMaternalCareOptimization.pdf contains the 10 parameter combinations that yielded the best adult fit, which then were subject to variation in the parameter phi to find the combination that yielded the best ontogenetic fit.</p> <p>The file 4runNotesDuplicates.pdf gives the parameter combinations that were not run because they are equivalent to other parameter combinations.</p> <p>Running [T,N1,run,Tshort,N1short,runShort]=etaCombinations in Matlab and typing run.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i (where i is a number 1,2,...) for PC-AC, EC-MC, PC-SC, and EC-SC. The meaning of "next" is explained in step 4 of the parameter sweep (section 5 of the SI). Typing runShort.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i for PC-MC and EC-AC.</p> <p>The terminal folders contain the solutions and have the following files:<br> brainNashDeep.m -> the master file launching the iteration of best responses.<br> brainMainDeep.m -> the file launching one iteration solving the optimal control problem to find best response.<br> brainMainTestRunDeep.m -> runs a test to check if there are infeasibility warnings.<br> brainContinuous.m -> specifies the dynamic constraints.<br> brainEndpoint.m -> specifies the terminal constraints.<br> parameters.m -> specifies the parameter values and rescales them to rescale units as specified in section 5 of the SI.<br> getSolution.m -> extracts solution.<br> plots.m -> plots solutions over best response iterations.<br> brainPlot.m -> plots solution of a given best response iteration.<br> guessDeep.mat -> initial guess and resident used.<br> solutionNashDeep.mat -> solutions over best response iterations.<br> solutionDeep.mat -> solution of last best response iteration.</p> <p> </p>
Excitation waves propagating on the streets of Barcelona. Numerical integration of Oregonator equations.
<p><a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_050.mov?versionId=285ba608-3f27-422f-b9f2-72137a5525a7">Barcelona_Raval_phi_0_050.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.050$<br> <br> <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_065.mov?versionId=30d2c271-9058-4587-a1ec-b5fdda78482b">Barcelona_Raval_phi_0_065.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.065$<br> <br> <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval_phi_0_074.mov?versionId=d0a4c922-b29c-46da-b8fd-618a404b482f">Barcelona_Raval_phi_0_074.mov</a>: Raval. Initial perturbation site is at the beginning of Les Rambles. $\phi=0.074$</p> <p>Barcelona_Gracia_phi_0_0666.mov: Gracia. Initial perturbation site is Sagrada Familia. $\phi=0.0666$</p> <p>====== Description of the model ====</p> <p>Two fragments of Barcelona street map --- Gracia and Raval, were mapped onto a grid of 2500 by 2500 nodes. Nodes of the grid corresponding to streets are considered to be filled with a Belousov-Zhabotinsky medium, i.e. excitable nodes, other nodes are non-excitable. We use two-variable Oregonator equations~\cite{field1974oscillations} adapted to a light-sensitive <br> Belousov-Zhabotinsky (BZ) reaction with applied illumination~\cite{beato2003pulse}:</p> <p>\begin{eqnarray}<br> \frac{\partial u}{\partial t} & = & \frac{1}{\epsilon} (u - u^2 - (f v + \phi)\frac{u-q}{u+q}) + D_u \nabla^2 u \nonumber \\<br> \frac{\partial v}{\partial t} & = & u - v <br> \label{equ:oregonator}<br> \end{eqnarray}</p> <p>The variables $u$ and $v$ represent local concentrations of an activator, or an excitatory component of BZ system, and an inhibitor, or a refractory component. Parameter $\epsilon$ sets up a ratio of the time scale of variables $u$ and $v$, $q$ is a scaling parameter depending on rates of activation/propagation and inhibition, $f$ is a stoichiometric coefficient. </p> <p> We integrated the system using Euler method with five-node Laplace operator, time step $\Delta t=0.001$ and grid point spacing $\Delta x = 0.25$, $\epsilon=0.02$, $f=1.4$, $q=0.002$. We varied value of $\phi$ from the interval $\Phi=[0.05,0.08]$.</p> <p>To generate excitation waves we perturb the medium by square solid domains of excitation, $20 \times 20$ sites in state $u=1.0$, site of the perturbation is shown by red discs in <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Gracia.png?versionId=cfeb3a2a-1f8a-427d-a2b1-41f9893a4d66">Barcelona_Gracia.png </a> and <a href="https://zenodo.org/api/files/38cdb802-1f50-4990-afbe-073fefbb46fe/Barcelona_Raval%20point.png?versionId=c0ad4d24-ff59-4287-a817-2c7bf72f509c">Barcelona_Raval point.png</a>. Time-lapse snapshots provided in the paper were recorded at every 150\textsuperscript{th} time step, we display sites with $u >0.04$; videos supplementing figures were produced by saving a frame of the simulation every 50\textsuperscript{th} step of numerical integration and assembling them in the video with play rate 30 fps. All figures in this paper show time lapsed snapshots of waves, initiated just once from a single source of stimulation; these are not trains of waves following each other.</p>
Data from: Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events
<p>This data is related to the seismic hammer experiment discussed in "Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events" by Martire et al. (2018, DOI will be added upon acceptance of the manuscript).</p> <p>The .zip file contains 3 .mseed files, and 1 .txt file. The .mseed are the raw seismometer signals. The .txt details the position of the sensor.</p> <p>Remaining data used in our paper can be found in the repository related to "Detection of Artificially Generated Seismic Signals using Balloon-borne Infrasound Sensor" by Krishnamoorthy et al. (2018, DOI 10.1002/2018GL077481). That repository has DOI 10.6084/m9.figshare.6137507.</p>
Numerical data on real-time brain signals
<p>Real-time brain signals database was collected during an intellectual competition. The Database was acquired via OpenVibe - a software platform dedicated to designing, testing and using brain- computer interfaces. This database shows brains activity during a specific period of time. As a result brain signals were acquired, filtered, processed, classified and visualized.</p> <p>This database can be used in medical (assistance to disabled people, real-time ), multimedia (virtual reality, video games), robotics and other purposes related to brain-computer interfaces and real-time neurosciences.</p>
Dataset of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors" (DOI: 10.1016/j.cej.2018.10.131)
<p>Datasets of Paper "Novel procedure for the numerical simulation of solar water disinfection processes in flow reactors".</p> <p>DOI: 10.1016/j.cej.2018.10.131</p> <p>Data of the velocity profiles at different distances from the inlet of a solar rainwater reactor.</p> <p>Data of the simulated radiation field inside of a solar rainwater reactor as a function of the location, date, time and CPC inclination.</p> <p>Data of the disinfection efficiency versus illumination time in a solar reactor under simulated and natural sunlight.</p>
Permeability Prediction in Rocks Experiencing Mineral Precipitation and Dissolution: A Numerical Study
<p>Data sets for the Publication 'Permeability Prediction in Rocks Experiencing Mineral Precipitation and Dissolution: A Numerical Study' in Water Resources Research.</p>
3D Taylor-Green vortex Direct Numerical Simulation statistics from Re=1250 to Re=20000
<p>Statistical data for the 3D Taylor Green flow from Re=1250 to Re=20000 obtained with the flow solver <a href="https://www.incompact3d.com/">Incompact3d</a>. </p> <p># ===========================================================================================<br> # When publishing results using this data, the following paper should be cited as the source: <br> # Thibault Dairay, Eric Lamballais, Sylvain Laizet and John Christos Vassilicos<br> # Numerical dissipation vs. subgrid-scale modelling for large eddy simulation<br> # Journal of Computational Physics 337 (2017) 252–274<br> # https://doi.org/10.1016/j.jcp.2017.02.035<br> # ===========================================================================================</p> <p># Column 1 : time t<br> # Column 2 : kinetic energy E_k [=(u^2+v^2+w^2)/2]<br> # Column 3 : dissipation epsilon_t [=-dE_k/dt]<br> # Column 4 : dissipation epsilon [= nu ((du/dx)^2+(du/dy)^2+(du/dz)^2+(dv/dx)^2+(dv/dy)^2+(dv/dz)^2+(dw/dx)^2+(dw/dy)^2+ dw/dz)^2)]<br> # Column 5 : enstrophy Dzeta [=2 nu epsilon]<br> # Column 6 : mean square u^2<br> # Column 7 : mean square v^2<br> # Column 8 : mean square w^2<br> # Column 9 : mean square (du/dx)^2<br> # Column 10 : mean square (du/dy)^2<br> # Column 11 : mean square (du/dz)^2<br> # Column 12 : mean square (dv/dx)^2<br> # Column 13 : mean square (dv/dy)^2<br> # Column 14 : mean square (dv/dz)^2<br> # Column 15 : mean square (dw/dx)^2<br> # Column 16 : mean square (dw/dy)^2<br> # Column 17 : mean square (dw/dz)^2</p>
The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility [complement to Supplementary Material 3]
<p>Raw data, and RStudio project, R markdown scripts and HTML outputs of the statistical procedures.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
The impact of biological bedforms on near-bed and subsurface flow: a laboratory validated numerical study of flow in the vicinity of pits and mounds
<p>The data were used in the paper "The impact of biological bedforms on near-bed and subsurface flow: a laboratory validated numerical study of flow in the vicinity of pits and mounds" which was submitted to "Journal of Geophysical Research-Earth Surface". In this paper, a novel, unified water-sediment three-dimensional model is developed to investigate the impact of simulated biogenic bedforms. The impact of biogenic bedforms on near-bed turbulence and sediment entrainment is discussed in gravelly substrates. Biogenic bedform morphology has an important role in determining the spatial extent of up- and down-welling flow.</p>
Numerical experiments for IOBM
<p>The data named "1-GRL-CAM-EXP1-40yr.nc" and "1-GRL-CAM-EXP2-40yr.nc" are the output of last 40 model years for EXP1 and EXP2, respectively, which are run for totally 60 model years. The data named "1-GRL-CAM-CRTL-40yr.nc" is the corresponding 40-yr data of CTRL run, which is used to compute the differences beween EXP1/EXP2 and CTRL.</p> <p>The data named "2-GRL-CAM-EXP3-30yr.nc" and "2-GRL-CAM-EXP4-30yr.nc" are the output of last 30 model years for EXP3 and EXP4, respectively, which are run for totally 45 model years. The data named "2-GRL-CAM-CRTL-30yr.nc" is the corresponding 30-yr data of CTRL run, which is used to compute the differences beween EXP3/EXP4 and CTRL.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.